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Availability is about access; reliability is about correct performance.

Database replication enhances data availability and read performance but has challenges like replication lag.

Database per Service ensures each microservice owns its data, reducing dependencies.

Fallbacks allow systems to handle failures safely and maintain user experience.

The dual-write trap in Elasticsearch can cause data inconsistency during reindexing.

Redis is a fast in-memory data store used for caching and low-latency applications.

Distributed locks ensure only one server performs a task, avoiding duplication.

The Bulkhead Pattern isolates resources to protect critical workloads from failures.

Cache penetration occurs when requests for non-existent data overload the database.

Caching helps applications respond faster by serving data from memory instead of hitting the database.

Distributed caching spreads data across multiple servers for better performance and reliability.

Elasticsearch uses shards for data distribution and replicas for redundancy.

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

Database bottlenecks slow down performance, but identifying them helps optimize and scale effectively.

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

The Cache-Aside Pattern speeds up data retrieval by using a cache to store frequently accessed data.

The RED Method helps monitor microservices using Rate, Errors, and Duration metrics.

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

The USE Method helps find and analyze infrastructure bottlenecks in software systems.